class_docstr: Independent multivariate Gaussian distribution parametrized by mean.
docstr: Initializes Gaussian distribution.
  
  Args:
    shape (tuple): Shape of the sample.
    std (float): Standard deviation, shared across the whole sample.
    learn_std (str or None): How to learn the standard deviation - 'shared'
      to have a single, shared std parameter, or 'separate' to have separate
      parameters for each dimension.
docstr_tok: ["Initializes","Gaussian","distribution",".","Args",":","shape","(","tuple",")",":","Shape","of","the","sample",".","std","(","float",")",":","Standard","deviation",",","shared","across","the","whole","sample",".","learn_std","(","str","or","None",")",":","How","to","learn","the","standard","deviation","-","'shared'","to","have","a","single",",","shared","std","parameter",",","or","'separate","'","to","have","separate","parameters","for","each","dimension","."]
code: def __init__(self, shape=(), std=1.0, learn_std=None):
      """Initializes Gaussian distribution.
  
      Args:
        shape (tuple): Shape of the sample.
        std (float): Standard deviation, shared across the whole sample.
        learn_std (str or None): How to learn the standard deviation - 'shared'
          to have a single, shared std parameter, or 'separate' to have separate
          parameters for each dimension.
      """
      self._shape = shape
      self._std = std
      self._learn_std = learn_std
  
imports: import gin
  import gym
  import numpy
  from trax import layers
  from trax.fastmath import numpy
label_desc: Use image node_id 1 to create a new Gaussian object from inherited base classes: Distribution with example: obj = Gaussian(shape, std, learn_std)